Lane boundary detection using radar signature trace data

ABSTRACT

A system, method, and computer-readable medium having instructions stored thereon to enable an ego vehicle having an autonomous driving function to estimate and traverse a curved segment of highway utilizing radar sensor data. The radar sensor data may comprise stationary reflections and moving reflections. The ego vehicle may utilize other data, such as global positioning system data, for the estimation and traversal. The estimation of the curvature may be refined based upon a lookup table or a deep neural network.

TECHNICAL FIELD

This disclosure relates to radar-based mapping and utilizing of radardata to improve autonomous driving functions of vehicles.

BACKGROUND

Radar-based mapping functions improve the operations of a vehicle withrespect to driver assistance or autonomous vehicle functions. Radarsensors are more common in modern vehicles than other forms of sensors,and thus it is desired to utilize radar data to improve reliable andsafe operation of driver assistance functions. In particular,determination of lane boundaries is very difficult using radar dataalone, as lane boundaries are easiest to detect using optical sensors.What is desired is a method and system for extrapolating lane boundariesfrom radar data.

SUMMARY

One aspect of this disclosure is directed to a method for navigating avehicle having an autonomous driving function through a curved segmentof a highway. The method may comprise capturing sensor data from asensor, such as a radar sensor, associated with the vehicle. The sensordata may comprise stationary reflection (SR) data indicating stationaryobjects and moving reflection (MR) data indicating moving objects. Themethod may further comprise estimating a width of a driving surface ofthe highway based on the SR data, estimating a curve radius of thehighway based upon the sensor data and the width of the driving surface,generating an estimated radar signature trace (RST) indicating atraversal curve for the vehicle to navigate, generating a lane positionof the vehicle with respect to the highway, and generating a refined RSTbased upon the sensor data, lane position, curve radius, and a lookuptable of road curvature data. Once the refined RST is generated, themethod may comprise navigating the vehicle along the refined RST throughthe curved segment of the highway.

Another aspect of this disclosure is directed to a non-transitorycomputer-readable medium having instructions stored thereon that whenexecuted by a processor associated with a vehicle having an autonomousdriving function cause the processor to perform a method for navigatingthe vehicle through a curved segment of a highway. The method maycomprise capturing sensor data from a sensor, such as a radar sensor,associated with the vehicle. The sensor data may comprise stationreflection (SR) data indicating stationary objects and moving reflection(MR) data indicating moving objects. The method may further compriseestimating a width of a driving surface of the highway based on the SRdata, estimating a curve radius of the highway based upon the sensordata and the width of the driving surface, generating an estimated radarsignature trace (RST) indicating a traversal curve for the vehicle tonavigate, generating a lane position of the vehicle with respect to thehighway, and generating a refined RST based upon the sensor data, laneposition, curve radius, and a lookup table of road curvature data. Oncethe refined RST is generated, the method may comprise navigating thevehicle along the refined RST through the curved segment of the highway.

A further aspect of this disclosure is directed to a vehicle navigationsystem associated with a vehicle having an autonomous driving function.The system may comprise a radar sensor operable to capture sensor datacomprising stationary reflection (SR) data indicating the location ofstationary objects with respect to the vehicle and moving reflection(MR) data indicating the location of moving objects with respect to thevehicle. The system may further comprise a process, a global positioningsystem (GPS) sensor associated with the vehicle and in datacommunication with the processor, and a memory in data communicationwith the processor. The memory may comprise processor-executableinstructions which, when executed by the processor, cause the processorto navigate the vehicle through a curved segment of a highway. Theinstructions may comprise the steps of estimating a width of a drivingsurface of the highway based on the SR data, estimating a curve radiusof the highway based upon the sensor data and the width of the drivingsurface, generating an estimated radar signature trace (RST) based uponthe sensor data, generating a lane position of the vehicle based uponthe SR data, GPS data, and high-density map data representing highways,generating a refined RST based upon the sensor data, lane position,curve radius, and a lookup table of road curvature data stored on thememory, and navigating the vehicle along the refined RST of the extentof the curved segment of highway.

Another aspect of this disclosure is directed to a method for navigatinga vehicle having an autonomous driving function through a curved segmentof a highway. The method may comprise capturing sensor data from asensor, such as a radar sensor, associated with the vehicle. The sensordata may comprise station reflection (SR) data indicating stationaryobjects and moving reflection (MR) data indicating moving objects. Themethod may further comprise estimating a curve radius of the highwaybased upon the sensor data generating an estimated radar signature trace(RST) indicating a traversal curve for the vehicle to navigate,generating a lane position of the vehicle with respect to the highwayfrom a deep neural network based on the SR data and generating a refinedRST based upon the sensor data, lane position, curve radius, and thedeep neural network. Once the refined RST is generated, the method maycomprise navigating the vehicle along the refined RST through the curvedsegment of the highway.

Another aspect of this disclosure is directed to a non-transitorycomputer-readable medium having instructions stored thereon that whenexecuted by a processor associated with a vehicle having an autonomousdriving function cause the processor to perform a method for navigatingthe vehicle through a curved segment of a highway. The method maycomprise capturing sensor data from a sensor, such as a radar sensor,associated with the vehicle. The sensor data may comprise stationreflection (SR) data indicating stationary objects and moving reflection(MR) data indicating moving objects. The method may further compriseestimating a curve radius of the highway based upon the sensor data,generating an estimated radar signature trace (RST) indicating atraversal curve for the vehicle to navigate, generating a lane positionof the vehicle with respect to the highway from a deep neural networkbased on the SR data, and generating a refined RST based upon the sensordata, lane position, curve radius, and the deep neural network. Once therefined RST is generated, the method may comprise navigating the vehiclealong the refined RST through the curved segment of the highway.

A further aspect of this disclosure is directed to a vehicle navigationsystem associated with a vehicle having an autonomous driving function.The system may comprise a radar sensor operable to capture sensor datacomprising stationary reflection (SR) data indicating the location ofstationary objects with respect to the vehicle and moving reflection(MR) data indicating the location of moving objects with respect to thevehicle. The system may further comprise a process, a global positioningsystem (GPS) sensor associated with the vehicle and in datacommunication with the processor, and a memory in data communicationwith the processor. The memory may comprise processor-executableinstructions which, when executed by the processor, cause the processorto navigate the vehicle through a curved segment of a highway. Theinstructions may comprise the steps of estimating a curve radius of thehighway based upon the sensor data and the width of the driving surface,generating an estimated radar signature trace (RST) based upon thesensor data, generating a lane position of the vehicle based upon the SRdata and a deep neural network trained on a historical corpus ofhistorical SR data and historical lane data, generating a refined RSTbased upon the sensor data, lane position, curve radius, and the deepneural network, and navigating the vehicle along the refined RST of theextent of the curved segment of highway.

The above aspects of this disclosure and other aspects will be explainedin greater detail below with reference to the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagrammatic illustration of a vehicle having an autonomousdriving function.

FIG. 2 is a diagrammatic illustration of a vehicle determining a radarsignature trace path on a straight segment of a highway.

FIG. 3 is a diagrammatic illustration of a vehicle determining a radarsignature trace path on a curved segment of a highway.

FIG. 4 is a flowchart illustrating a method of generating a radarsignature trace path on a curved segment of highway utilizing a lookuptable.

FIG. 5 is an example subset of data found in a lookup table useful forgenerating a radar signature trace path on a curved segment of highway.

FIG. 6 is a flowchart illustrating a method of generating a radarsignature trace path on a curved segment of highway utilizing a deepneural network.

FIG. 7 is a flowchart illustrating a method for training a deep neuralnetwork useful for generating a radar signature trace path on a curvedsegment of highway.

DETAILED DESCRIPTION

The illustrated embodiments are disclosed with reference to thedrawings. However, it is to be understood that the disclosed embodimentsare intended to be merely examples that may be embodied in various andalternative forms. The figures are not necessarily to scale and somefeatures may be exaggerated or minimized to show details of particularcomponents. The specific structural and functional details disclosed arenot to be interpreted as limiting, but as a representative basis forteaching one skilled in the art how to practice the disclosed concepts.

FIG. 1 is a diagrammatic illustration of a vehicle 100 having anautonomous driving function via a processor 101. Processor 101 may beconfigured to control functions and components of vehicle 100. Theautonomous driving function of vehicle 100 may comprise a fullyautonomous driving mode, one or more advanced driver assistancefunctions, or may optionally select between modes. In some embodimentsof vehicle 100, the autonomous driving function may be disable at thediscretion of an operator without deviating from the teachings disclosedherein.

In the depicted embodiment, processor 100 comprises a processing deviceassociated with vehicle 100 and permanently installed within thevehicle. However, in other embodiments, processor 100 may be embodied asa mobile processing device, a smartphone, a tablet computer, a laptopcomputer, a wearable computing device, a desktop computer, a personaldigital assistant (PDA) device, a handheld processor device, aspecialized processor device, a system of processors distributed acrossa network, a system of processors configured in wired or wirelesscommunication, or any other alternative embodiment known to one ofordinary skill in the art.

Processor 101 is in data communication with a number of components ofvehicle 100, and may utilize this data communication to acquire datanecessary to safely and successfully execute functions of the vehicle,or may use the data communication to exert direct control of one or moreof the components. A memory 103 may be associated with vehicle 100 andin data communication with processor 101. Memory 103 may provideinstructions for the processor 101 to execute, such the instructions forthe processor to successfully control the autonomous function orfunctions of vehicle 100. Memory 103 may also comprise data storage orprovide data to processor 101 to utilize in operation.

In the depicted embodiment, memory 103 may comprise embedded memoryassociated with vehicle 100 and installed therein. However, in otherembodiments, memory 103 may be embodied as a non-transitorycomputer-readable storage medium or a machine-readable medium forcarrying or having computer-executable instructions or data structuresstored thereon. Such non-transitory computer-readable storage media ormachine-readable medium may be any available media embodied in ahardware or physical form that can be accessed by a general purpose orspecial purpose computer. By way of example, and not limitation, suchnon-transitory computer-readable storage media or machine-readablemedium may comprise random-access memory (RAM), read-only memory (ROM),electrically erasable programmable read-only memory (EEPROM), opticaldisc storage, magnetic disk storage, linear magnetic data storage,magnetic storage devices, flash memory, or any other medium which can beused to carry or store desired program code means in the form ofcomputer-executable instructions or data structures. Combinations of theabove should also be included within the scope of the non-transitorycomputer-readable storage media or machine-readable medium.

Processor 101 may be in additional data communication with a number ofsensors operable to provide measurements indicating conditions of thevehicle 100, or the surroundings of the vehicle during operation. In thedepicted embodiment, vehicle 100 comprises a number of sensors includingradar sensors 105, camera sensors 107, and a global positioning system(GPS) system 109. In the depicted embodiment, vehicle 100 comprisesmultiple radar sensors 105 a and 105 b arranged at the front and rear ofthe vehicle, but other embodiments may have a different number of radarsensors or a different arrangement of radar sensors without deviatingfrom the teachings disclosed herein. Radar sensors 105 may be operableto measure radar sensor data indicating the position of moving andstationary objects relative to vehicle 100. Radar sensors 105 may beconfigured to emit a radar signal and generate sensor data indicatingthe relative time and directionality of reflections of the radar signalto indicate objects within the environment. The sensor data may becompiled across iterative radar transmissions and detections to providea more complete and robust imagining of the environment. Such sensordata may be classified based upon the detection of movement of themeasured objects within the environment. Measurements indicating amoving object are classified as moving reflection (MR) data.Measurements indicating a stationary object are classified as stationaryreflection (SR) data. Analysis of the sensor data may be completed, suchas by processor 101, in view of the current moving speed of vehicle 100to provide an assessment of the motion of detected objects relative tothe instant position and motion of vehicle 100. In some suchembodiments, processor 101 may utilize data received from a speedometeror other instrument configured to generate speed data reflective of themoving speed of vehicle 100 to accomplish the classification of MR dataand SR data.

In the depicted embodiment, vehicle 100 comprises multiple camerasensors 107 a and 107 b arranged at the front and rear of the vehicle,but other embodiments may have a different number of camera sensors or adifferent arrangement of camera sensors without deviating from theteachings disclosed herein. Camera sensors 107 may provide redundantsensor data in a different form that can be analyzed to detect objectswithin the environment. Such redundant measurements may be utilized toimprove the accuracy and reliability of object detection with respect tovehicle 100. Camera sensors 107 may additionally advantageously beutilized to detect features of the environment that are not easilydetected or measured using radar data, or using radar data alone. Insome embodiments, camera sensors 107 may be utilized to detect laneboundaries on the surface of a highway, identify text or numerals withinsignage, or to assist in an object identification function of detectedobjects without deviating from the teachings disclosed herein. Otherutility for camera data may be recognized by those of ordinary skillwithout deviating from the teachings disclosed herein. Some embodimentsmay not comprise camera sensors 107 without deviating from the teachingsdisclosed herein.

GPS sensor 109 may be operable to measure a global location of vehicle100 and generate GPS data comprising GPS coordinates indicating such aposition. GPS sensor 109 may provide this GPS data to processor 101 toassist in executing functions of the processor or other functions ofvehicle 100 controlled by processor 101. Some embodiments may have adifferent number or arrangement of GPS sensors 109 without deviatingfrom the teachings disclosed herein. Some embodiments may utilize otherlocalization protocols than GPS to provide localization data describingthe location of vehicle 100 without deviating from the teachingsdisclosed herein. Some embodiments may not have a GPS sensor 109 orfunctional equivalent without deviating from the teachings disclosedherein.

In some instances, it may be advantageous for processor 101 to be incommunications with devices external to vehicle 100. Such communicationsmay be utilized to deliver data describing vehicle 100 to an externaldevice, or to acquire data from external sources that may be utilized inthe operations of vehicle 100. To accommodate such communicationsbetween processor 101 and external devices, vehicle 100 may furthercomprise a wireless transceiver 111 in data communication with processor101.

Wireless transceiver 111 may be configured to communicate wirelessly viaone or more of an RF (radio frequency) specification, cellular phonechannels (analog or digital), cellular data channels, a Bluetoothspecification, a Wi-Fi specification, a satellite transceiverspecification, infrared transmission, a Zigbee specification, Local AreaNetwork (LAN), Wireless Local Area Network (WLAN), or any otheralternative configuration, protocol, or standard known to one ofordinary skill in the art. In the depicted embodiment, wirelesstransceiver 111 comprises a single device that is operable to send datato and receive data from external sources wirelessly, but otherembodiments may comprise distinct components acting as a transmitter anda receiver respectively without deviating from the teachings disclosedherein. Other embodiments may comprise a different arrangement or numberof transceivers, transmitters, or receivers without deviating from theteachings disclosed herein.

Vehicle 100 may comprise a number of autonomous driving functions. Suchfunctions may advantageously have improved safety and reliability ifoperated with additional knowledge about the vehicle 100 or theoperating environment. One example of desirable additional knowledge maybe the position of the boundaries of a highway surface upon which thevehicle 100 is operating. Another example of desirable knowledge may bethe position of any lane boundaries defined upon a surface of a highwayupon which the vehicle 100 is operating. Because of the relativeaffordability and prevalence of radar sensors, it would be advantageousto extrapolate the boundaries of the highway and any associated laneboundaries utilizing radar sensors.

FIG. 2 is a diagrammatic illustration of a vehicle 100 acting as aso-called “ego vehicle” in an operation to determine the boundaries of ahighway road surface 200 using radar reflection data. In the depictedoperation, vehicle 100 is moving along road surface 200 in an egodirection 202 at an arbitrary (but known) speed. When in motion or whilestopped, the sensors of vehicle 100 (such as radar sensors 105, see FIG.1 ) may be utilized to detect stationary and moving objects based uponreflections. In the depicted illustration, stationary objects areindicated by stationary reflections (SR) 203 which are measured by thesensors of vehicle 100 to generate SR data. In the depictedillustration, moving objects are indicated by moving reflections (MR)205 which are measured by the sensors of vehicle 100 to generate MRdata. The relative position of objects with respect to vehicle 100 maybe determined after multiple measurements by the sensors. When suchmeasurements are made in view of the active speed of vehicle 100, it maybe determined which of the reflections are SR and which of thereflections are MR.

In the depicted illustration, MR 205 may be determined to be travelingin an MR direction 206. In some embodiments, the system may utilize MRdata to assess the likelihood that MR 205 corresponds to another vehicleon the road, or another moving object utilizing the roadway, such as amotorcycle, bicycle, or pedestrian. This assessment may be based uponthe detected size of the object indicated by the MR data, and also beestimating the relative speed of the moving object or objects that areresponsible for the proliferation of MR 205.

In the depicted illustration, vehicle 100 may utilize the sensor data tomake generate a road estimation 209 indicating an estimated width of adrivable surface of the road. In the depicted embodiment, thisestimation may utilize both SR data and MR data, but in some embodimentsonly SR data may be utilized. In such embodiments, the road estimation209 may be predicated on an assumption that there will no substantialobjects within the width of the highway, and thus the SR data isunderstood only to correspond to objects placed only on surfaces in theproximity of the road where it is not legal to drive, such as theshoulder.

In the depicted illustration, vehicle 100 may utilize the sensor data togenerate a radar signature trace (RST) 211, providing an estimated pathof traversal along the highway for the vehicle 100 to maneuver. The RST211 should desirably keep the vehicle 100 on the drivable surface of thehighway and making progress in a desired direction, such as egodirection 202. In the depicted embodiment, the road surface 200 maycomprise multiple lanes, and the processor 101 (see FIG. 1 ) may performa series of calculations to determine how many lanes are expected for ahighway of estimated width 209. The expected number of lanes for ahighway may be determined utilizing data indicating highway regulationsof the immediate vicinity, such as regulations defining required roadwidths and lane widths. In some embodiments, processor 101 may acquireadditional data to assist in determining the number of lanes present inthe immediate vicinity. Such data may comprise, for example, GPScoordinates acquired by GPS sensor 109 (see FIG. 1 ) and high-densitymap data. The GPS coordinates may be cross-referenced with thehigh-density map data to determine an expected number of lanes forvehicle 100 to observe in the instant location. The high-density mapdata may be locally stored, such as on memory 103, or may be acquiredfrom an external source via wireless transceiver 111.

After a number of expected lanes is determined, RST 211 may be generatedin view of the expected number of lanes, and may utilize laneestimations 212 to help position RST 211 within the legal bounds of anestimated lane position. Such positioning of the RST 211 mayadvantageously improve the likelihood that vehicle 100 will remainwithin the legal bounds of a lane (and within the legal bounds of roadsurface 200) when traversing RST 211.

In the illustration of FIG. 2 , road surface 200 comprises a relativelystraight section of highway. A straight section of highway mayadvantageously provide very accurate estimations of road surface 200because the SR data may indicate a correspondingly straight and regulardistance from the edges of road surface 200. Thus, in a straight sectionof highway, processor 101 may be able to utilize the sensor data, andeven rely upon SR data primarily, to generate relatively accurateestimated lane boundaries 213. Notably, in sections of road that areunderstood to comprise only a single lane, the lane boundaries 213 mayindicate the legal boundaries for the driving surface of the highway.

FIG. 3 provides a diagrammatic illustration of a different road surface300 in which comprises a more complicated environment for developing anRST in the form of a curved section of highway. In this depictedillustration, the SR 203 are not position at regular and even distancesfrom the edges of a straight road surface 300, in part because roadsurface 300 curves m a manner consistent with a curve radius 301. Insuch embodiments, processor 101 (see FIG. 1 ) may generate an estimatedRST 303 that does not successfully create a trajectory that vehicle 100may follow that successfully contains the vehicle within the legalboundaries of the road surface.

Processor 101 may utilize additional data analysis to refill thetrajectory of RST 303. For example, in some embodiments processor 101may utilize MR 205 to measure how the object producing the reflectionstraverses the curvature of the road. The reflections within MR 205 maybe compiled as MR data that can be utilized to generate a RST associatedwith MR 205 (not shown). However, while utilization of MR data may beuseful to refine the estimated RST 303, reliance, upon this data is notpreferred for at least two reasons. Firstly, it is possible, and in someinstances likely, that no MR data will be available because no othermoving objects are in the vicinity of vehicle 100. Secondly, the motionof objects indicated by MR data may itself not conform exactly to thecurve of road surface 300. For example, MR 205 may correspond to anothervehicle being driven by a human driver that has a tendency to “cut thecurve” of turns necessary to stay on the driving surface of the road,and therefore the associated vehicle may not reliably stay within thelanes itself. In other instances, MR 205 may correspond to other movingobjects that are not conforming to the lane boundaries on purpose, suchas during a lane change maneuver. In any such instances, it is stilldesired for the “ego” vehicle 100 to remain within the bounds of its ownlane.

These challenges may be overcome by taking into account a curve radius301 of the road surface 300. Curve radius 301 expresses the radius of anarc exhibiting the same curve as road surface 300. Curve radius 301 mayadvantageously be calculated utilizing SR data alone. In the depictedembodiment, the SR 203 may be measured at various points along roadsurface 300 to determine estimated widths 309 of road surface 300 ateach instantaneous point of measurement. In the depicted example, the SRdata may be utilized to estimate an orientation of each of the estimatedwidths 309 as well. The estimated widths and orientation of the road canthen be compared to known dimensions of regulated road construction toestimate curve radius 301. Other data may be utilized to assist in theestimation of curve radius 301, such as high-density map data of theroad based upon GPS data. GPS data may be acquired by a GPS sensor, suchas GPS sensor 109 (see FIG. 1 ). The high-density map data may be storedin a memory local to vehicle 100, such as memory 103 (see FIG. 1 ), ormay be acquired from an external source. Other embodiments may, estimatecurve radius 301 utilizing different techniques without deviating fromthe teachings disclosed herein.

Utilizing the SR data and curve radius 301, vehicle 100 may generate arefined RST 315 that provides a path to traverse road surface 300through the curved segment of the highway. Refined RST 315 may begenerated by utilizing additional information, such as MR data, otherRSTs corresponding to the MR data, GPS data, or any other datarecognized by one of ordinary skill in the art to improve the accuracyof the refined RST 315 without deviating from the teachings disclosedherein.

Vehicle 100 may be configured to utilize one of several techniques togenerate a refined RST 315 and successfully navigate the generated RST.In some embodiments, an ego vehicle 100 may utilize data from a lookuptable comprising historical data of successful turns on roads havingsimilar dimensions and configurations as the instant road beingtraversed by the ego vehicle. In such embodiments, the lookup table maybe populated with RST data derived from previously-captured SR data. Thelookup table may additionally be populated based upon additional data,such as MR data or visually-confirmed optical data describing successfulRSTs through curved segments of highways. The optical data may beacquired using optical measuring devices, such as camera sensors 107(see FIG. 1 ). In such embodiments, an ego vehicle 100 having both radarsensors 105 and camera sensors 107 may generate corresponding data fromeach type of sensor and provide the data to external processors tocontinuously improve the populated data of the lookup table. However, insome embodiments, the lookup table may remain static once populated, ormay instead be updated in a regular (but non-continuous) fashion withoutdeviating from the teachings disclosed herein.

FIG. 4 is a flowchart of a method to be followed by a processor, such asprocessor 101 (see FIG. 1 ) to control an ego vehicle having anautonomous function through a curved segment of highway. The ego vehicletraverses the curve by following a radar signature trace (RST) throughthe curve. An ideal RST though the curve would exhibit the mostefficient path to traverse the curve without the vehicle driving outsideof a lane boundary. The method utilizes sensor data and a lookup tableto generate a refined RST for the ego vehicle to traverse.

The method begins at step 400, where the ego vehicle utilizes sensors tocapture sensor data. In the depicted embodiment, the sensors comprise atleast radar sensors providing stationary reflections (SR) indicating theposition of stationary objects relative to the ego vehicle.Advantageously, the radar sensors may also provide moving reflectionsindicating the position and motion of moving objects relative to the egovehicle. The ego vehicle may comprise other sensors providing otherdata, such as a global positioning system (GPS) sensor providing GPSdata indicating a location of the ego vehicle. The ego vehicle maycomprise additional sensors, such as optical sensors, camera sensors,lidar sensors, or other sensors known in the art without deviating fromthe teachings disclosed herein.

After collecting sensor data, the method proceeds to step 402, where thesensor data is analyzed to determine if a curve has been detected. Ifnot, the method returns to step 400. If yes, then the method proceeds tostep 404, where the sensor data is utilized to estimate the width of thehighway's driving surface. The width of the driving surface may beestimated using SR data, but MR data, GPS data, or other data may beutilized to improve the estimation. After the width of the drivingsurface has been estimated, the method proceeds to step 406, and thecurve radius of the highway to be traversed is estimated.

After the curve radius of the highway is estimated at step 406, themethod continues to step 408, where an estimated RST is generated. Inother embodiments, this estimated RST may be utilized to traverse thecurve, but in the current embodiment, the method seeks to refine thisRST using previously-acquired knowledge presented in a lookup table.Thus, the method continues to step 410 where the lane position of theego vehicle is acquired. For roadways having a single lane (or a singlelane for the direction of travel of the ego vehicle), this step maycomprise estimating the center of the portion of roadway accommodatingthe ego vehicle. For multi-lane roadways, a calculation of the number oflanes may be first required, with the relative position of the egovehicle with respect to the SR data providing an estimation of which ofthe lanes of the determined number of lanes corresponding to the lane inwhich the ego vehicle is moving. In some embodiments, the calculation ofthe number of lanes may be based upon the estimated road width in viewof road regulations. In some embodiments, the ego vehicle may utilizeadditional data, such as GPS data, to improve the accuracy of the laneposition acquisition. For example, GPS coordinates of the ego vehiclemay be compared to high-density map data indicating a number of lanesassociated with the road at the GPS coordinates. Utilizing thesecoordinates may therefore improve the accuracy of the acquired laneposition.

The method may then move to the next step 412, where the lookup table isaccessed and the contents are compared to the acquired data for roadwidth, curve radius, and lane position. The acquired data mayadditionally comprise a curvature direction for the turn of the road.All of this data may be compared to the lookup table, which comprisescurvature data defining curved segments of highways. In someembodiments, historical SR data or historical MR data may be includedwithin the curvature data, and may be compared to the captured data aswell.

The curvature data may be utilized in the next step 414 where a refinedRST is generated that corresponds to the curvature within the lookuptable that most closely-resembles the data acquired and estimated thusfar in the method. Once the refined RST has been generated, the methodcontinues to step 416 where the ego vehicle navigates along the refinedRST.

In the depicted embodiment, the method will next determine if the egovehicle has successfully traversed the entirety of the curved segment ofhighway. If not, the method may return to step 404 and proceed throughthe method to generate an additional refined RST for the additionallength of the curved segment. If the curve has been completed, themethod may end at step 420. In other embodiments, instead of terminatingthe method, the method may instead return to step 400 (not shown) untilanother curve is detected by the sensor data.

The lookup table of the method may comprise a collection of curvaturedata defining curves according to known curved segments of highway. Inthe depicted embodiment, the lookup table may be populated with a knownset of data points describing the known curved segments of highway.These data may have been acquired by extrapolating from road-buildingregulations. In the depicted embodiment, these data may additionallyhave been acquired using historical data, such as real-worldmeasurements of curve radius and road width. In such embodiments, thehistorical data may additionally comprise historical SR data or MR data.In such embodiments, the road curvature data may have been acquiredbased upon other types of real-world measurements using other sensors,such as camera sensors, optical sensors, or lidar sensors.

The curvature data of the lookup table may comprise descriptions of RSTscorresponding to distinct lane positions, curve directions, or curvepositions within the curved segment of highway. By way of example, andnot limitation, the curve positions may be one of an entry or onset ofthe curve, a middle of the curve, and an egress or exit of the curve.These distinctions of curve positions may advantageously accommodatedifferent RST trajectories with increased accuracy to maintain the egovehicle within the legal boundaries of the road.

In some embodiments, the curvature data of the lookup table may beupdated according to additional measurements of curved segments ofhighway in real time. By way of example, and not limitation, the curvedsegments of highway may be successfully traversed by an ego vehicle, andthe ego vehicle may store the associated measurements and estimated datapertaining to the resulting RST in a memory. The memory may be local tothe ego vehicle, such as memory 103 (see FIG. 1 ), or may be transmittedto an external memory wireless, such as by wireless transceiver 111 (seeFIG. 1 ). Updates of the lookup table may be regularly scheduled, suchas occurring after a set period of time or a set distance driven by anego vehicle. Updates to the lookup table may be triggered by drivingevents, such as disengaging the prime mover of an ego vehicle whenfinished driving. Updates to the lookup table may occur continuouslywhile driving. Other update conditions may be utilized by the egovehicle without deviating from the teachings disclosed herein. Other egovehicles may have access to the lookup table without deviating from theteachings within. In such embodiments, all the vehicles with access tothe lookup table may form a fleet of vehicles, and some or all of thefleet may be configured to provide updates to the lookup table. In someembodiments, updates to the look up table may be provided by data entryfrom an authorized user of the system, such as via a data transfer ormanual input of curvature data, without deviating from the teachingsdisclosed herein.

FIG. 5 provides an example of data found within a lookup table, such asthe lookup table utilized in the method of FIG. 4 . In the depictedembodiment, it is shown that the curvature data defines a curve radius,a lane position of the ego vehicle, a curve direction, a curve position,and distances to a left boundary and a right boundary. The left boundaryand right boundary may comprise lane boundaries, or legal boundaries forthe road surface (e.g., the shoulder, or a lane servicing oncomingtraffic). In the depicted embodiment, the curvature data may be definedto describe rightmost or leftmost lane positions, but other data maydescribe other lane positions without deviating from the teachingsdisclosed herein. In the depicted embodiment, the curvature data may bedefined to describe positions within the curve, such as a curve-entry; acurve-middle, or curve-egress, but other embodiments may compriseadditional curve positions without deviating from the teachingsdisclosed herein.

Lookup table refinement may advantageously be simple and inexpensive toimplement, but are heavily dependent upon the quality of data providedby the lookup table, and improvements to the RST refinement arerelatively static between updates of the lookup table. Other embodimentsmay utilize machine learning techniques to provide faster improvement ofthe RST generation.

FIG. 6 is a flowchart of a method to be followed by a processor, such asprocessor 101 (see FIG. 1 ) to control an ego vehicle having anautonomous function through a curved segment of highway. The ego vehicletraverses the curve by following a radar signature trace (RST) throughthe curve. An ideal RST though the curve would exhibit the mostefficient path to traverse the curve without the vehicle driving outsideof a lane boundary. The method utilizes sensor data and a machinelearning techniques to generate a refined RST for the ego vehicle totraverse. In the depicted embodiment, the machine learning techniqueutilized may comprise a deep neural network (DNN).

The method begins at step 600, where the ego vehicle utilizes sensors tocapture sensor data. In the depicted embodiment, the sensors comprise atleast radar sensors providing stationary reflections (SR) indicating theposition of stationary objects relative to the ego vehicle.Advantageously, the radar sensors may also provide moving reflectionsindicating the position and motion of moving objects relative to the egovehicle. The ego vehicle may comprise other sensors providing otherdata, such as a global positioning system (GPS) sensor providing GPSdata indicating a location of the ego vehicle. The ego vehicle maycomprise additional sensors, such as optical sensors, camera sensors,lidar sensors, or other sensors known in the art without deviating fromthe teachings disclosed herein.

After collecting sensor data, the method proceeds to step 602, where thesensor data is analyzed to determine if a curve has been detected. Ifnot, the method returns to step 600. If yes, then the method proceeds tostep 604, where the sensor data is utilized to estimate the width of thehighway's driving surface. The width of the driving surface may beestimated using SR data, but MR data, GPS data, or other data may beutilized to improve the estimation. After the width of the drivingsurface has been estimated, the method proceeds to step 606, and thecurve radius of the highway is estimated.

After the curve radius of the highway is estimated, the method proceedsto step 608 which generates an estimated RST. In the depictedembodiment, the estimated RST may be generated based upon the road widthand the cure radius. Other data, such as SR data or GPS data may beutilized to improve the accuracy of the estimated RST. In someembodiments, the estimated RST may be generated in view of received MRdata.

Once an estimated RST is generated, the method proceeds to step 610 andthe estimated RST is sent to a deep neural network (DNN) for analysis.The DNN may be trained on historical RST data and boundary data that isknown to correlate to real-world curved segments of highway. Theestimated RST may be analyzed by the DNN to generate lane estimations inreal-time. Using these lane estimations, a refined RST may be generatedat step 612 that optimizes the trajectory through the curved segment ofhighway. The optimized refined RST may be generated such that it wouldcause an ego vehicle to traverse the curve as efficiently as possiblewithout travelling outside the lane boundaries. This refined RST havingoptimal characteristics is used to navigate the ego vehicle at step 614.After the navigation has occurred with the RST traversal, the methodproceeds to step 616 where a determination is made as to whether thecurve of the highway has been completely traversed. If not, the methodmay return to step 606. If the curve has been traversed, the method mayproceed to step 618 to end.

An advantage of a deep neural network over other methods of refinementis that the DNN may be updated in response to successful completions ofthe method. Such updates provide a form of continual training andrefinement of the DNN over time to better reflect real-world conditionsencountered by vehicles. In the depicted embodiment, an optional step620 may be performed after the completion of a curve, wherein the DNN isupdated using some or all of the data measured, acquired, or estimatedearlier in the method. The data utilized to update the DNN may varydepending on the exact configuration of the DNN. However, someembodiments of the invention may utilize some or all of the SR data, MRdata, GPS data estimated RST, and refined RST to update the DNN. In someembodiments, the DNN may weight the additional data in the real-timetraining to fine-tune its response in a manner desired for optimizedresults. This weighting may give additional weight to more recent dataacquired, or to refined RSTs that prove to have a high degree of successstaying in the center of a lane. Other weighting schemes recognized bythose of ordinary skill may be utilized without deviating from theteachings disclosed herein.

In some embodiments, additional data may be utilized to train the DNN.For example, an ego vehicle having camera sensors (such as camerasensors 107; see FIG. 1 ) may provide sensor data indicating the exactlane boundaries of the road, and this additional data may be used totrack the accuracy of the refined RSTs. In some embodiments, a pluralityof ego vehicles may be in wireless communication with the DNN, and theDNN may be updated rising data provided by some or all of the vehicleswithout deviating from the teachings disclosed herein. Such embodimentscan advantageously improve their refinement very rapidly because of thehigh volume of data provided over time to continuously train and updatethe DNN.

Before the DNN can be utilized to generate a refined RST, it must firstbe trained to recognize and refine RSTs using frown data. FIG. 7 shows aflowchart illustrating a training phase for the DNN that may be utilizedprior to implementation. The training phase relies upon two inputs: afirst step of the training phase 700 provides a set of RST trainingdata, and a second step 702 provides a corresponding set of label data.The RST training data may a corpus of sample RST curvature data, but insome embodiments may additionally comprise SR data or MR data if the DNNis configured to generate an initial estimated RST. The label data ofstep 702 comprises data indicating road boundaries and lane boundariesthat are known to accurately reflect the desired estimations of the DNNwhen provided with the corresponding RST training data. In the depictedembodiment, the label data may comprise data acquired from opticalsensors, such as camera sensors 105 (see FIG. 1 ). In some embodiments,the label data may not be derived from sensor measurements, and mayinstead be input by hand or from a corpus of pre-selected data known tobe an accurate representation of curved segments of highways.

In the depicted embodiment, it is preferable for the RST training datain step 700 and corresponding label data in step 702 to represent avariety of expected road conditions and configurations. Curved segmentsof roads having different curve radii, road widths, number of lanes, andcurve directions can advantageously train the resulting DNN to beadaptable to a wide variety of real-world conditions.

At step 704, the RST training data is provided to the deep neuralnetwork for assessment and estimation of the lane boundariescorresponding to the RST training data. After training, the estimatedlane boundaries can be utilized to generate refined RSTs byinterpolating the mid-points of a lane in which an ego vehicle istraveling as it traverses through a curved segment of highway. For thisreason, it is desirable for the DNN to generate estimated boundary datathat is as accurate to the real-world conditions as possible. Thus, theDNN at step 704 generates estimated boundary data that is compared tothe label data of step 702 in comparison with a cost function at step706. In the depicted embodiment, the estimated lane boundaries areoptimized when the cost function yields a minimum cost in thecomparison. After a cost has been generated, a training optimizer atstep 708 may adjust the operations of the DNN for any portion of theestimated boundary data that exhibits a cost above a threshold valuewhen compared to the label data. The adjusted DNN is thus trained tominimize the cost for the lane estimations for each set of RST trainingdata and corresponding boundary data. This process of estimation,comparison, and optimization may be continued until the results havebeen optimized for all RST training data and corresponding boundarydata.

Other data may be utilized to further optimize the trainingeffectiveness. In some such embodiments, the label data may additionallycomprise SR data corresponding to the known boundaries. In suchembodiments, the RST training data may additionally comprise SR trainingdata that may be used by the DNN to estimate lane boundaries andgenerate refined RSTs. Such an approach may be advantageous because itdoes not necessarily rely exclusively on lane estimations to generate arefined RST, and may instead utilize the more reliably-measured SR dataas an input as well.

In some embodiments, the RST training data may comprise MR trainingdata, and the label data may additionally comprise a historical corpusof corresponding MR data. In such embodiments, the historical corpus ofMR data may advantageously be utilized to further optimize the DNN byproviding examples of how other vehicles may be observed to traversecurved segments of highways. Such examples may be utilized by thetraining optimizer of step 708 to train the DNN to recognize MR data asadditional useable information in generated estimated lane boundaries orrefined RSTs.

The different embodiments disclosed herein may provide differentadvantages in implementation for an ego vehicle. By way of example, andnot limitation, the lookup table implementation may be inexpensive andfast to implement, whereas the deep neural network may be comparativelymore accurate and improves faster over time. Some embodiments maycomprise a hybrid approach to generating a refined RST without deviatingfrom the teachings disclosed herein. Other embodiments may compriseother techniques in addition to one or more of a lookup table or a deepneural network to optimize the generation of a refined RST for an egovehicle.

While exemplary embodiments are described above, it is not intended thatthese embodiments describe all possible forms of the disclosed apparatusand method. Rather, the words used in the specification are words ofdescription rather than limitation, and it is understood that variouschanges may be made without departing from the spirit and scope of thedisclosure as claimed. The features of various implementing embodimentsmay be combined to form further embodiments of the disclosed concepts.Such portions of highways having traffic moving at relatively constantspeeds and generally in the same direction are ideal environments toutilize an RST 211 because of these regular and predictable behaviors.

What is claimed is:
 1. A method for navigating a vehicle having anautonomous driving function through a curved segment of a highway, themethod comprising: capturing sensor data from a sensor associated withthe vehicle, the sensor data comprising stationary reflection (SR) dataindicating stationary objects and moving reflection (MR) data indicatingmoving objects; estimating a width of a driving surface of the highwaybased on the SR data; estimating a curve radius of the highway basedupon the sensor data and the width of the driving surface; generating anestimated radar signature trace (RST) indicating a traversal curve forthe vehicle to navigate based upon the sensor data; acquiring globalpositioning system (GPS) coordinates for the vehicle; acquiring a lanecount indicating the number of lanes on the highway based on the GPScoordinates and high-density map data; generating a lane position of thevehicle based upon the SR data and the lane count; generating a refinedRST based upon the sensor data, lane position, curve radius, and alookup table of road curvature data; and navigating the vehicle alongthe refined RST through the curved segment of the highway.
 2. The methodof claim 1, wherein the estimated RST comprises an estimatedcurve-entry, an estimated curve-middle, and an estimated curve-egressand the refined RST comprises a refined curve-entry, a refinedcurve-middle, and a refined curve-egress.
 3. The method of claim 2,further comprising increasing a weight of the road curvature datacorresponding to the refined RST in the lookup table upon completion ofthe navigating the vehicle along the refined RST.
 4. The method of claim1, wherein the high-density map data is delivered to the vehiclewirelessly from a remote data storage device.
 5. The method of claim 1,wherein generating the refined RST further comprises acquiringhistorical MR data indicating previously-measured MR data correspondingto curved segments of highways having the same curve radius andgenerating the refined RST based upon the historical MR data.
 6. Themethod of claim 5, further comprising adding the MR data to thehistorical MR data upon completion of the step of navigating the vehiclealong the refined RST.
 7. The method of claim 1, wherein the lookuptable of curvature initially comprises road curvature data acquired fromvehicles having a camera sensor.
 8. A non-transitory computer-readablemedium having instructions stored thereon that when executed by aprocessor associated with a vehicle having an autonomous drivingfunction cause the processor to perform a method for navigating thevehicle through a curved segment of a highway, the method comprising:capturing sensor data from a sensor associated with the vehicle, thesensor data comprising stationary reflection (SR) data indicatingstationary objects and moving reflection (MR) data indicating movingobjects; estimating a width of a driving surface of the highway based onthe SR data; estimating a curve radius of the highway based upon thesensor data and the width of the driving surface; generating anestimated radar signature trace (RST) indicating a traversal curve forthe vehicle to navigate based upon the sensor data; acquiring globalpositioning system (GPS) coordinates for the vehicle; acquiring a lanecount indicating the number of lanes on the highway based on the GPScoordinates and high-density map data; generating a lane position of thevehicle based upon the SR data and the lane count; generating a refinedRST based upon the sensor data, lane position, curve radius, and alookup table of road curvature data; and navigating the vehicle alongthe refined RST through the curved segment of highway.
 9. Thenon-transitory computer-readable medium of claim 8, wherein theestimated RST comprises an estimated curve-entry, an estimatedcurve-middle, and an estimated curve-egress and the refined RSTcomprises a refined curve-entry, a refined curve-middle, and a refinedcurve-egress.
 10. The non-transitory computer-readable medium of claim9, wherein the instructions further comprise adding the refined RST tothe lookup table upon completion of the navigating the vehicle along therefined RST.
 11. The non-transitory computer-readable medium of claim 8,wherein the generating the refined RST further comprises acquiringhistorical MR data indicating previously-measured MR data correspondingto curved segments of highway having the same curve radius andgenerating the refined RST based upon the historical MR data.
 12. Thenon-transitory computer-readable medium of claim 11, wherein theinstructions further comprise adding the MR data to the historical MRdata upon completion of the step of navigating the vehicle along therefined RST.
 13. The non-transitory computer-readable medium of claim 8,wherein the lookup table of curvature data is initially populated usinga corpus of data acquired from vehicles having a camera sensor.
 14. Avehicle navigation system associated with a vehicle having an autonomousdriving function, the system comprising: a radar sensor operable tocapture sensor data associated with the vehicle, the sensor datacomprising stationary reflection (SR) data indicating the location ofstationary objects with respect to the vehicle and moving reflection(MR) data indicating the location of moving objects with respect to thevehicle; a processor in data communication with the radar sensor; aglobal positioning system (GPS) sensor associated with the vehicle andin data communication with the processor, the GPS sensor configured togenerate GPS data associated with the vehicle; and a memory in datacommunication with the processor, wherein the processor is configured toexecute instructions stored on the memory to navigate the vehiclethrough a curved segment of highway by estimating a width of a drivingsurface of the highway based on the SR data, estimating a curve radiusof the highway based upon the sensor data and the width of the drivingsurface, generating an estimated radar signature trace (RST) based uponthe sensor data, generating a lane position of the vehicle based uponthe SR data, GPS data, and high-density map data representing highways,generating a refined RST based upon the sensor data, lane position,curve radius, and a lookup table of road curvature data stored on thememory, and navigating the vehicle along the refined RST of the extentof the curved segment of highway.
 15. The system of claim 14, whereinthe estimated RST comprises an estimated curve-entry, an estimatedcurve-middle, and an estimated curve-egress and the refined RSTcomprises a refined curve-entry, a refined curve-middle, and a refinedcurve-egress.
 16. The system of claim 15, wherein the processor isfurthers operable to add the refined RST to the lookup table uponcompletion of the navigating the vehicle along the refined RST.
 17. Thesystem of claim 14, further comprising a wireless transceiver associatedwith the vehicle and in data communication with the processor, andwherein the high-density map data is delivered from a remote datastorage device via the wireless transceiver.
 18. The system of claim 14,wherein processor is further operable to generate the refined RST byacquiring historical MR data indicating previously-measured MR datacorresponding to curved segments of highway having the same curve radiusand generating the refined RST based upon the historical MR data. 19.The system of claim 14, wherein the lookup table of curvature data isinitially populated using a corpus of data acquired from vehicles havinga camera sensor.
 20. The system of claim 19, wherein the processor isfurther operable to add the refined RST to the lookup table uponcompletion of the navigating the vehicle along the refined RST.